25 citations · 66 across the 16 of their papers we have counts for
19 papers
Gibbs optimal design of experiments
Antony M. Overstall, Jacinta Holloway-Brown, James M. McGree
Bayesian optimal design is a well-established approach to planning experiments. A distribution for the responses, i.e. a statistical model, is assumed which is dependent on unknown…
Zero-Truncated Modelling in a Meta-Analysis on Suicide Data after Bariatric Surgery
Layna Charlie Dennett, Antony Overstall, Dankmar Boehning
Meta-analysis is a well-established method for integrating results from several independent studies to estimate a common quantity of interest. However, meta-analysis is prone to se…
A general Bayesian approach to design adaptive clinical trials with time-to-event outcomes
James M. McGree, Antony M. Overstall, Mark Jones +1
Clinical trials are an integral component of medical research. Trials require careful design to, for example, maintain the safety of participants, use resources efficiently and all…
Robust simulation design for generalized linear models in conditions of heteroscedasticity or correlation
Andrew Gill, David J. Warne, Antony M. Overstall +2
A meta-model of the input-output data of a computationally expensive simulation is often employed for prediction, optimization, or sensitivity analysis purposes. Fitting is enabled…
Optimal design of dynamic experiments for scalar-on-function linear models with application to a biopharmaceutical study
Damianos Michaelides, Maria Adamou, David C. Woods +1
A Bayesian optimal experimental design framework is developed for experiments where settings of one or more variables, referred to as profile variables, can be functions. For this…
General Bayesian L2 calibration of mathematical models
Antony M. Overstall, James M. McGree
A mathematical model is a function taking certain arguments and returning a theoretical prediction of a feature of a physical system. The arguments to the mathematical model can be…